PV power output prediction from sky images using convolutional neural network: The comparison of sky-condition-specific sub-models and an end-to-end model-Reference-Cited by-同舟云学术

PV power output prediction from sky images using convolutional neural network: The comparison of sky-condition-specific sub-models and an end-to-end model

Published:2020-07 Issue:4 Volume:12 Page:046101
ISSN:1941-7012
Container-title:Journal of Renewable and Sustainable Energy
language:en
Short-container-title:Journal of Renewable and Sustainable Energy

Author:

Nie Yuhao¹^ORCID,Sun Yuchi¹,Chen Yuanlei¹^ORCID,Orsini Rachel¹,Brandt Adam¹^ORCID

Affiliation:

1. Department of Energy Resources Engineering, Stanford University, Stanford, California 94305, USA

Funder

Dubai Electricity and Water Authority

Publisher

AIP Publishing

Subject

Renewable Energy, Sustainability and the Environment

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0014016

Reference27 articles.

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2. Research opportunities to advance solar energy utilization

3. Net-zero emissions energy systems

4. International Renewable Energy Agency, “ Global energy transformation: A roadmap to 2050 (2019 edition),” ( Abu Dhabi, 2019).

5. Y. Sun , “ Short-term solar forecast using convolutional neural networks with sky images,” Ph.D. dissertation ( Stanford University, 2019).

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